Textual Analysis of the DigComp 3.0 Framework Using Frequency and TF–IDF Methods

The recently released DigComp 3.0 framework represents the latest evolution of the European reference model for digital competences, expanding its descriptors, proficiency levels, and learning outcomes. This study applies text mining and natural language processing (NLP) techniques to systematically analyse the textual content of the framework across all its dimensions, including competence descriptions, proficiency level statements, and learning outcomes. After performing standard text pre-processing steps—such as normalization, tokenization, and filtering—we extracted general statistics on the structure of the framework and conducted keyword analysis using term frequency and TF–IDF methods. These techniques enabled the identification of the most meaningful terms and action verbs that characterize the formulation of digital competences in DigComp 3.0. The aim of this research is to provide an evidence-based linguistic perspective on the framework’s internal structure, highlight its conceptual emphases, and support future comparative studies or data-driven enhancements of digital competence models.